Ermitteln Sie Informationen in Videos mithilfe von Amazon Rekognition und AWS SDK

Abrufen von Informationen aus einem Video, das sich in einem Amazon-S3-Bucket befindet.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartCelebrityRecognitionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.CelebrityRecognitionSortBy;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.CelebrityRecognition;
import software.amazon.awssdk.services.rekognition.model.CelebrityDetail;
import software.amazon.awssdk.services.rekognition.model.StartCelebrityRecognitionRequest;
import software.amazon.awssdk.services.rekognition.model.GetCelebrityRecognitionRequest;
import software.amazon.awssdk.services.rekognition.model.GetCelebrityRecognitionResponse;
import java.util.List;

/**
 * To run this code example, ensure that you perform the Prerequisites as stated
 * in the Amazon Rekognition Guide:
 * https://docs.aws.amazon.com/rekognition/latest/dg/video-analyzing-with-sqs.html
 *
 * Also, ensure that set up your development environment, including your
 * credentials.
 *
 * For information, see this documentation topic:
 *
 * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
 */

public class VideoCelebrityDetection {
    private static String startJobId = "";

    public static void main(String[] args) {
        final String usage = """

                Usage:    <bucket> <video> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of video (for example, people.mp4).\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 4) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String topicArn = args[2];
        String roleArn = args[3];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startCelebrityDetection(rekClient, channel, bucket, video);
        getCelebrityDetectionResults(rekClient);
        System.out.println("This example is done!");
        rekClient.close();
    }

    public static void startCelebrityDetection(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartCelebrityRecognitionRequest recognitionRequest = StartCelebrityRecognitionRequest.builder()
                    .jobTag("Celebrities")
                    .notificationChannel(channel)
                    .video(vidOb)
                    .build();

            StartCelebrityRecognitionResponse startCelebrityRecognitionResult = rekClient
                    .startCelebrityRecognition(recognitionRequest);
            startJobId = startCelebrityRecognitionResult.jobId();

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }

    public static void getCelebrityDetectionResults(RekognitionClient rekClient) {

        try {
            String paginationToken = null;
            GetCelebrityRecognitionResponse recognitionResponse = null;
            boolean finished = false;
            String status;
            int yy = 0;

            do {
                if (recognitionResponse != null)
                    paginationToken = recognitionResponse.nextToken();

                GetCelebrityRecognitionRequest recognitionRequest = GetCelebrityRecognitionRequest.builder()
                        .jobId(startJobId)
                        .nextToken(paginationToken)
                        .sortBy(CelebrityRecognitionSortBy.TIMESTAMP)
                        .maxResults(10)
                        .build();

                // Wait until the job succeeds
                while (!finished) {
                    recognitionResponse = rekClient.getCelebrityRecognition(recognitionRequest);
                    status = recognitionResponse.jobStatusAsString();

                    if (status.compareTo("SUCCEEDED") == 0)
                        finished = true;
                    else {
                        System.out.println(yy + " status is: " + status);
                        Thread.sleep(1000);
                    }
                    yy++;
                }

                finished = false;

                // Proceed when the job is done - otherwise VideoMetadata is null.
                VideoMetadata videoMetaData = recognitionResponse.videoMetadata();
                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());
                System.out.println("Job");

                List<CelebrityRecognition> celebs = recognitionResponse.celebrities();
                for (CelebrityRecognition celeb : celebs) {
                    long seconds = celeb.timestamp() / 1000;
                    System.out.print("Sec: " + seconds + " ");
                    CelebrityDetail details = celeb.celebrity();
                    System.out.println("Name: " + details.name());
                    System.out.println("Id: " + details.id());
                    System.out.println();
                }

            } while (recognitionResponse.nextToken() != null);

        } catch (RekognitionException | InterruptedException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

Erkennen Sie Labels in einem Video mithilfe einer Labelerkennung.

import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.JsonMappingException;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.LabelDetectionSortBy;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.LabelDetection;
import software.amazon.awssdk.services.rekognition.model.Label;
import software.amazon.awssdk.services.rekognition.model.Instance;
import software.amazon.awssdk.services.rekognition.model.Parent;
import software.amazon.awssdk.services.sqs.SqsClient;
import software.amazon.awssdk.services.sqs.model.Message;
import software.amazon.awssdk.services.sqs.model.ReceiveMessageRequest;
import software.amazon.awssdk.services.sqs.model.DeleteMessageRequest;
import java.util.List;

/**
 * Before running this Java V2 code example, set up your development
 * environment, including your credentials.
 *
 * For more information, see the following documentation topic:
 *
 * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
 */
public class VideoDetect {
    private static String startJobId = "";

    public static void main(String[] args) {
        final String usage = """

                Usage:    <bucket> <video> <queueUrl> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of the video (for example, people.mp4).\s
                   queueUrl- The URL of a SQS queue.\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 5) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String queueUrl = args[2];
        String topicArn = args[3];
        String roleArn = args[4];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        SqsClient sqs = SqsClient.builder()
                .region(Region.US_EAST_1)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startLabels(rekClient, channel, bucket, video);
        getLabelJob(rekClient, sqs, queueUrl);
        System.out.println("This example is done!");
        sqs.close();
        rekClient.close();
    }

    public static void startLabels(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartLabelDetectionRequest labelDetectionRequest = StartLabelDetectionRequest.builder()
                    .jobTag("DetectingLabels")
                    .notificationChannel(channel)
                    .video(vidOb)
                    .minConfidence(50F)
                    .build();

            StartLabelDetectionResponse labelDetectionResponse = rekClient.startLabelDetection(labelDetectionRequest);
            startJobId = labelDetectionResponse.jobId();

            boolean ans = true;
            String status = "";
            int yy = 0;
            while (ans) {

                GetLabelDetectionRequest detectionRequest = GetLabelDetectionRequest.builder()
                        .jobId(startJobId)
                        .maxResults(10)
                        .build();

                GetLabelDetectionResponse result = rekClient.getLabelDetection(detectionRequest);
                status = result.jobStatusAsString();

                if (status.compareTo("SUCCEEDED") == 0)
                    ans = false;
                else
                    System.out.println(yy + " status is: " + status);

                Thread.sleep(1000);
                yy++;
            }

            System.out.println(startJobId + " status is: " + status);

        } catch (RekognitionException | InterruptedException e) {
            e.getMessage();
            System.exit(1);
        }
    }

    public static void getLabelJob(RekognitionClient rekClient, SqsClient sqs, String queueUrl) {
        List<Message> messages;
        ReceiveMessageRequest messageRequest = ReceiveMessageRequest.builder()
                .queueUrl(queueUrl)
                .build();

        try {
            messages = sqs.receiveMessage(messageRequest).messages();

            if (!messages.isEmpty()) {
                for (Message message : messages) {
                    String notification = message.body();

                    // Get the status and job id from the notification
                    ObjectMapper mapper = new ObjectMapper();
                    JsonNode jsonMessageTree = mapper.readTree(notification);
                    JsonNode messageBodyText = jsonMessageTree.get("Message");
                    ObjectMapper operationResultMapper = new ObjectMapper();
                    JsonNode jsonResultTree = operationResultMapper.readTree(messageBodyText.textValue());
                    JsonNode operationJobId = jsonResultTree.get("JobId");
                    JsonNode operationStatus = jsonResultTree.get("Status");
                    System.out.println("Job found in JSON is " + operationJobId);

                    DeleteMessageRequest deleteMessageRequest = DeleteMessageRequest.builder()
                            .queueUrl(queueUrl)
                            .build();

                    String jobId = operationJobId.textValue();
                    if (startJobId.compareTo(jobId) == 0) {
                        System.out.println("Job id: " + operationJobId);
                        System.out.println("Status : " + operationStatus.toString());

                        if (operationStatus.asText().equals("SUCCEEDED"))
                            getResultsLabels(rekClient);
                        else
                            System.out.println("Video analysis failed");

                        sqs.deleteMessage(deleteMessageRequest);
                    } else {
                        System.out.println("Job received was not job " + startJobId);
                        sqs.deleteMessage(deleteMessageRequest);
                    }
                }
            }

        } catch (RekognitionException e) {
            e.getMessage();
            System.exit(1);
        } catch (JsonMappingException e) {
            e.printStackTrace();
        } catch (JsonProcessingException e) {
            e.printStackTrace();
        }
    }

    // Gets the job results by calling GetLabelDetection
    private static void getResultsLabels(RekognitionClient rekClient) {

        int maxResults = 10;
        String paginationToken = null;
        GetLabelDetectionResponse labelDetectionResult = null;

        try {
            do {
                if (labelDetectionResult != null)
                    paginationToken = labelDetectionResult.nextToken();

                GetLabelDetectionRequest labelDetectionRequest = GetLabelDetectionRequest.builder()
                        .jobId(startJobId)
                        .sortBy(LabelDetectionSortBy.TIMESTAMP)
                        .maxResults(maxResults)
                        .nextToken(paginationToken)
                        .build();

                labelDetectionResult = rekClient.getLabelDetection(labelDetectionRequest);
                VideoMetadata videoMetaData = labelDetectionResult.videoMetadata();
                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());

                List<LabelDetection> detectedLabels = labelDetectionResult.labels();
                for (LabelDetection detectedLabel : detectedLabels) {
                    long seconds = detectedLabel.timestamp();
                    Label label = detectedLabel.label();
                    System.out.println("Millisecond: " + seconds + " ");

                    System.out.println("   Label:" + label.name());
                    System.out.println("   Confidence:" + detectedLabel.label().confidence().toString());

                    List<Instance> instances = label.instances();
                    System.out.println("   Instances of " + label.name());

                    if (instances.isEmpty()) {
                        System.out.println("        " + "None");
                    } else {
                        for (Instance instance : instances) {
                            System.out.println("        Confidence: " + instance.confidence().toString());
                            System.out.println("        Bounding box: " + instance.boundingBox().toString());
                        }
                    }
                    System.out.println("   Parent labels for " + label.name() + ":");
                    List<Parent> parents = label.parents();

                    if (parents.isEmpty()) {
                        System.out.println("        None");
                    } else {
                        for (Parent parent : parents) {
                            System.out.println("   " + parent.name());
                        }
                    }
                    System.out.println();
                }
            } while (labelDetectionResult != null && labelDetectionResult.nextToken() != null);

        } catch (RekognitionException e) {
            e.getMessage();
            System.exit(1);
        }
    }
}

Erkennen von Gesichtern in einem Video, das in einem Amazon-S3-Bucket gespeichert ist.

import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.JsonMappingException;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartLabelDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.GetLabelDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.LabelDetectionSortBy;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.LabelDetection;
import software.amazon.awssdk.services.rekognition.model.Label;
import software.amazon.awssdk.services.rekognition.model.Instance;
import software.amazon.awssdk.services.rekognition.model.Parent;
import software.amazon.awssdk.services.sqs.SqsClient;
import software.amazon.awssdk.services.sqs.model.Message;
import software.amazon.awssdk.services.sqs.model.ReceiveMessageRequest;
import software.amazon.awssdk.services.sqs.model.DeleteMessageRequest;
import java.util.List;

/**
 * Before running this Java V2 code example, set up your development
 * environment, including your credentials.
 *
 * For more information, see the following documentation topic:
 *
 * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
 */
public class VideoDetect {
    private static String startJobId = "";

    public static void main(String[] args) {
        final String usage = """

                Usage:    <bucket> <video> <queueUrl> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of the video (for example, people.mp4).\s
                   queueUrl- The URL of a SQS queue.\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 5) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String queueUrl = args[2];
        String topicArn = args[3];
        String roleArn = args[4];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        SqsClient sqs = SqsClient.builder()
                .region(Region.US_EAST_1)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startLabels(rekClient, channel, bucket, video);
        getLabelJob(rekClient, sqs, queueUrl);
        System.out.println("This example is done!");
        sqs.close();
        rekClient.close();
    }

    public static void startLabels(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartLabelDetectionRequest labelDetectionRequest = StartLabelDetectionRequest.builder()
                    .jobTag("DetectingLabels")
                    .notificationChannel(channel)
                    .video(vidOb)
                    .minConfidence(50F)
                    .build();

            StartLabelDetectionResponse labelDetectionResponse = rekClient.startLabelDetection(labelDetectionRequest);
            startJobId = labelDetectionResponse.jobId();

            boolean ans = true;
            String status = "";
            int yy = 0;
            while (ans) {

                GetLabelDetectionRequest detectionRequest = GetLabelDetectionRequest.builder()
                        .jobId(startJobId)
                        .maxResults(10)
                        .build();

                GetLabelDetectionResponse result = rekClient.getLabelDetection(detectionRequest);
                status = result.jobStatusAsString();

                if (status.compareTo("SUCCEEDED") == 0)
                    ans = false;
                else
                    System.out.println(yy + " status is: " + status);

                Thread.sleep(1000);
                yy++;
            }

            System.out.println(startJobId + " status is: " + status);

        } catch (RekognitionException | InterruptedException e) {
            e.getMessage();
            System.exit(1);
        }
    }

    public static void getLabelJob(RekognitionClient rekClient, SqsClient sqs, String queueUrl) {
        List<Message> messages;
        ReceiveMessageRequest messageRequest = ReceiveMessageRequest.builder()
                .queueUrl(queueUrl)
                .build();

        try {
            messages = sqs.receiveMessage(messageRequest).messages();

            if (!messages.isEmpty()) {
                for (Message message : messages) {
                    String notification = message.body();

                    // Get the status and job id from the notification
                    ObjectMapper mapper = new ObjectMapper();
                    JsonNode jsonMessageTree = mapper.readTree(notification);
                    JsonNode messageBodyText = jsonMessageTree.get("Message");
                    ObjectMapper operationResultMapper = new ObjectMapper();
                    JsonNode jsonResultTree = operationResultMapper.readTree(messageBodyText.textValue());
                    JsonNode operationJobId = jsonResultTree.get("JobId");
                    JsonNode operationStatus = jsonResultTree.get("Status");
                    System.out.println("Job found in JSON is " + operationJobId);

                    DeleteMessageRequest deleteMessageRequest = DeleteMessageRequest.builder()
                            .queueUrl(queueUrl)
                            .build();

                    String jobId = operationJobId.textValue();
                    if (startJobId.compareTo(jobId) == 0) {
                        System.out.println("Job id: " + operationJobId);
                        System.out.println("Status : " + operationStatus.toString());

                        if (operationStatus.asText().equals("SUCCEEDED"))
                            getResultsLabels(rekClient);
                        else
                            System.out.println("Video analysis failed");

                        sqs.deleteMessage(deleteMessageRequest);
                    } else {
                        System.out.println("Job received was not job " + startJobId);
                        sqs.deleteMessage(deleteMessageRequest);
                    }
                }
            }

        } catch (RekognitionException e) {
            e.getMessage();
            System.exit(1);
        } catch (JsonMappingException e) {
            e.printStackTrace();
        } catch (JsonProcessingException e) {
            e.printStackTrace();
        }
    }

    // Gets the job results by calling GetLabelDetection
    private static void getResultsLabels(RekognitionClient rekClient) {

        int maxResults = 10;
        String paginationToken = null;
        GetLabelDetectionResponse labelDetectionResult = null;

        try {
            do {
                if (labelDetectionResult != null)
                    paginationToken = labelDetectionResult.nextToken();

                GetLabelDetectionRequest labelDetectionRequest = GetLabelDetectionRequest.builder()
                        .jobId(startJobId)
                        .sortBy(LabelDetectionSortBy.TIMESTAMP)
                        .maxResults(maxResults)
                        .nextToken(paginationToken)
                        .build();

                labelDetectionResult = rekClient.getLabelDetection(labelDetectionRequest);
                VideoMetadata videoMetaData = labelDetectionResult.videoMetadata();
                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());

                List<LabelDetection> detectedLabels = labelDetectionResult.labels();
                for (LabelDetection detectedLabel : detectedLabels) {
                    long seconds = detectedLabel.timestamp();
                    Label label = detectedLabel.label();
                    System.out.println("Millisecond: " + seconds + " ");

                    System.out.println("   Label:" + label.name());
                    System.out.println("   Confidence:" + detectedLabel.label().confidence().toString());

                    List<Instance> instances = label.instances();
                    System.out.println("   Instances of " + label.name());

                    if (instances.isEmpty()) {
                        System.out.println("        " + "None");
                    } else {
                        for (Instance instance : instances) {
                            System.out.println("        Confidence: " + instance.confidence().toString());
                            System.out.println("        Bounding box: " + instance.boundingBox().toString());
                        }
                    }
                    System.out.println("   Parent labels for " + label.name() + ":");
                    List<Parent> parents = label.parents();

                    if (parents.isEmpty()) {
                        System.out.println("        None");
                    } else {
                        for (Parent parent : parents) {
                            System.out.println("   " + parent.name());
                        }
                    }
                    System.out.println();
                }
            } while (labelDetectionResult != null && labelDetectionResult.nextToken() != null);

        } catch (RekognitionException e) {
            e.getMessage();
            System.exit(1);
        }
    }
}

Erkennen von unangemessenen oder anstößigen Inhalten in einem Video, das in einem Amazon-S3-Bucket gespeichert ist.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartContentModerationRequest;
import software.amazon.awssdk.services.rekognition.model.StartContentModerationResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.GetContentModerationResponse;
import software.amazon.awssdk.services.rekognition.model.GetContentModerationRequest;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.ContentModerationDetection;
import java.util.List;

/**
 * Before running this Java V2 code example, set up your development
 * environment, including your credentials.
 *
 * For more information, see the following documentation topic:
 *
 * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
 */
public class VideoDetectInappropriate {
    private static String startJobId = "";

    public static void main(String[] args) {

        final String usage = """

                Usage:    <bucket> <video> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of video (for example, people.mp4).\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 4) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String topicArn = args[2];
        String roleArn = args[3];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startModerationDetection(rekClient, channel, bucket, video);
        getModResults(rekClient);
        System.out.println("This example is done!");
        rekClient.close();
    }

    public static void startModerationDetection(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {

        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartContentModerationRequest modDetectionRequest = StartContentModerationRequest.builder()
                    .jobTag("Moderation")
                    .notificationChannel(channel)
                    .video(vidOb)
                    .build();

            StartContentModerationResponse startModDetectionResult = rekClient
                    .startContentModeration(modDetectionRequest);
            startJobId = startModDetectionResult.jobId();

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }

    public static void getModResults(RekognitionClient rekClient) {
        try {
            String paginationToken = null;
            GetContentModerationResponse modDetectionResponse = null;
            boolean finished = false;
            String status;
            int yy = 0;

            do {
                if (modDetectionResponse != null)
                    paginationToken = modDetectionResponse.nextToken();

                GetContentModerationRequest modRequest = GetContentModerationRequest.builder()
                        .jobId(startJobId)
                        .nextToken(paginationToken)
                        .maxResults(10)
                        .build();

                // Wait until the job succeeds.
                while (!finished) {
                    modDetectionResponse = rekClient.getContentModeration(modRequest);
                    status = modDetectionResponse.jobStatusAsString();

                    if (status.compareTo("SUCCEEDED") == 0)
                        finished = true;
                    else {
                        System.out.println(yy + " status is: " + status);
                        Thread.sleep(1000);
                    }
                    yy++;
                }

                finished = false;

                // Proceed when the job is done - otherwise VideoMetadata is null.
                VideoMetadata videoMetaData = modDetectionResponse.videoMetadata();
                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());
                System.out.println("Job");

                List<ContentModerationDetection> mods = modDetectionResponse.moderationLabels();
                for (ContentModerationDetection mod : mods) {
                    long seconds = mod.timestamp() / 1000;
                    System.out.print("Mod label: " + seconds + " ");
                    System.out.println(mod.moderationLabel().toString());
                    System.out.println();
                }

            } while (modDetectionResponse != null && modDetectionResponse.nextToken() != null);

        } catch (RekognitionException | InterruptedException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

Erkennen Sie technische Signal-Segmente und Einstellungserkennungssegmente in einem Video, das in einem Amazon-S3-Bucket gespeichert ist.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartShotDetectionFilter;
import software.amazon.awssdk.services.rekognition.model.StartTechnicalCueDetectionFilter;
import software.amazon.awssdk.services.rekognition.model.StartSegmentDetectionFilters;
import software.amazon.awssdk.services.rekognition.model.StartSegmentDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.StartSegmentDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.GetSegmentDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.GetSegmentDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.SegmentDetection;
import software.amazon.awssdk.services.rekognition.model.TechnicalCueSegment;
import software.amazon.awssdk.services.rekognition.model.ShotSegment;
import software.amazon.awssdk.services.rekognition.model.SegmentType;
import software.amazon.awssdk.services.sqs.SqsClient;
import java.util.List;

/**
 * Before running this Java V2 code example, set up your development
 * environment, including your credentials.
 *
 * For more information, see the following documentation topic:
 *
 * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
 */
public class VideoDetectSegment {
    private static String startJobId = "";

    public static void main(String[] args) {
        final String usage = """

                Usage:    <bucket> <video> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of video (for example, people.mp4).\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 4) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String topicArn = args[2];
        String roleArn = args[3];

        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        SqsClient sqs = SqsClient.builder()
                .region(Region.US_EAST_1)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startSegmentDetection(rekClient, channel, bucket, video);
        getSegmentResults(rekClient);
        System.out.println("This example is done!");
        sqs.close();
        rekClient.close();
    }

    public static void startSegmentDetection(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartShotDetectionFilter cueDetectionFilter = StartShotDetectionFilter.builder()
                    .minSegmentConfidence(60F)
                    .build();

            StartTechnicalCueDetectionFilter technicalCueDetectionFilter = StartTechnicalCueDetectionFilter.builder()
                    .minSegmentConfidence(60F)
                    .build();

            StartSegmentDetectionFilters filters = StartSegmentDetectionFilters.builder()
                    .shotFilter(cueDetectionFilter)
                    .technicalCueFilter(technicalCueDetectionFilter)
                    .build();

            StartSegmentDetectionRequest segDetectionRequest = StartSegmentDetectionRequest.builder()
                    .jobTag("DetectingLabels")
                    .notificationChannel(channel)
                    .segmentTypes(SegmentType.TECHNICAL_CUE, SegmentType.SHOT)
                    .video(vidOb)
                    .filters(filters)
                    .build();

            StartSegmentDetectionResponse segDetectionResponse = rekClient.startSegmentDetection(segDetectionRequest);
            startJobId = segDetectionResponse.jobId();

        } catch (RekognitionException e) {
            e.getMessage();
            System.exit(1);
        }
    }

    public static void getSegmentResults(RekognitionClient rekClient) {
        try {
            String paginationToken = null;
            GetSegmentDetectionResponse segDetectionResponse = null;
            boolean finished = false;
            String status;
            int yy = 0;

            do {
                if (segDetectionResponse != null)
                    paginationToken = segDetectionResponse.nextToken();

                GetSegmentDetectionRequest recognitionRequest = GetSegmentDetectionRequest.builder()
                        .jobId(startJobId)
                        .nextToken(paginationToken)
                        .maxResults(10)
                        .build();

                // Wait until the job succeeds.
                while (!finished) {
                    segDetectionResponse = rekClient.getSegmentDetection(recognitionRequest);
                    status = segDetectionResponse.jobStatusAsString();

                    if (status.compareTo("SUCCEEDED") == 0)
                        finished = true;
                    else {
                        System.out.println(yy + " status is: " + status);
                        Thread.sleep(1000);
                    }
                    yy++;
                }
                finished = false;

                // Proceed when the job is done - otherwise VideoMetadata is null.
                List<VideoMetadata> videoMetaData = segDetectionResponse.videoMetadata();
                for (VideoMetadata metaData : videoMetaData) {
                    System.out.println("Format: " + metaData.format());
                    System.out.println("Codec: " + metaData.codec());
                    System.out.println("Duration: " + metaData.durationMillis());
                    System.out.println("FrameRate: " + metaData.frameRate());
                    System.out.println("Job");
                }

                List<SegmentDetection> detectedSegments = segDetectionResponse.segments();
                for (SegmentDetection detectedSegment : detectedSegments) {
                    String type = detectedSegment.type().toString();
                    if (type.contains(SegmentType.TECHNICAL_CUE.toString())) {
                        System.out.println("Technical Cue");
                        TechnicalCueSegment segmentCue = detectedSegment.technicalCueSegment();
                        System.out.println("\tType: " + segmentCue.type());
                        System.out.println("\tConfidence: " + segmentCue.confidence().toString());
                    }

                    if (type.contains(SegmentType.SHOT.toString())) {
                        System.out.println("Shot");
                        ShotSegment segmentShot = detectedSegment.shotSegment();
                        System.out.println("\tIndex " + segmentShot.index());
                        System.out.println("\tConfidence: " + segmentShot.confidence().toString());
                    }

                    long seconds = detectedSegment.durationMillis();
                    System.out.println("\tDuration : " + seconds + " milliseconds");
                    System.out.println("\tStart time code: " + detectedSegment.startTimecodeSMPTE());
                    System.out.println("\tEnd time code: " + detectedSegment.endTimecodeSMPTE());
                    System.out.println("\tDuration time code: " + detectedSegment.durationSMPTE());
                    System.out.println();
                }

            } while (segDetectionResponse != null && segDetectionResponse.nextToken() != null);

        } catch (RekognitionException | InterruptedException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

Erkennen Sie Text in einem Video, das in einem Amazon-S3-Bucket gespeichert ist.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartTextDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.StartTextDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.GetTextDetectionResponse;
import software.amazon.awssdk.services.rekognition.model.GetTextDetectionRequest;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.TextDetectionResult;
import java.util.List;

/**
 * Before running this Java V2 code example, set up your development
 * environment, including your credentials.
 *
 * For more information, see the following documentation topic:
 *
 * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
 */
public class VideoDetectText {
    private static String startJobId = "";

    public static void main(String[] args) {
        final String usage = """

                Usage:    <bucket> <video> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of video (for example, people.mp4).\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 4) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String topicArn = args[2];
        String roleArn = args[3];

        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startTextLabels(rekClient, channel, bucket, video);
        getTextResults(rekClient);
        System.out.println("This example is done!");
        rekClient.close();
    }

    public static void startTextLabels(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartTextDetectionRequest labelDetectionRequest = StartTextDetectionRequest.builder()
                    .jobTag("DetectingLabels")
                    .notificationChannel(channel)
                    .video(vidOb)
                    .build();

            StartTextDetectionResponse labelDetectionResponse = rekClient.startTextDetection(labelDetectionRequest);
            startJobId = labelDetectionResponse.jobId();

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }

    public static void getTextResults(RekognitionClient rekClient) {
        try {
            String paginationToken = null;
            GetTextDetectionResponse textDetectionResponse = null;
            boolean finished = false;
            String status;
            int yy = 0;

            do {
                if (textDetectionResponse != null)
                    paginationToken = textDetectionResponse.nextToken();

                GetTextDetectionRequest recognitionRequest = GetTextDetectionRequest.builder()
                        .jobId(startJobId)
                        .nextToken(paginationToken)
                        .maxResults(10)
                        .build();

                // Wait until the job succeeds.
                while (!finished) {
                    textDetectionResponse = rekClient.getTextDetection(recognitionRequest);
                    status = textDetectionResponse.jobStatusAsString();

                    if (status.compareTo("SUCCEEDED") == 0)
                        finished = true;
                    else {
                        System.out.println(yy + " status is: " + status);
                        Thread.sleep(1000);
                    }
                    yy++;
                }

                finished = false;

                // Proceed when the job is done - otherwise VideoMetadata is null.
                VideoMetadata videoMetaData = textDetectionResponse.videoMetadata();
                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());
                System.out.println("Job");

                List<TextDetectionResult> labels = textDetectionResponse.textDetections();
                for (TextDetectionResult detectedText : labels) {
                    System.out.println("Confidence: " + detectedText.textDetection().confidence().toString());
                    System.out.println("Id : " + detectedText.textDetection().id());
                    System.out.println("Parent Id: " + detectedText.textDetection().parentId());
                    System.out.println("Type: " + detectedText.textDetection().type());
                    System.out.println("Text: " + detectedText.textDetection().detectedText());
                    System.out.println();
                }

            } while (textDetectionResponse != null && textDetectionResponse.nextToken() != null);

        } catch (RekognitionException | InterruptedException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

Erkennen Sie Personen in einem Video, das in einem Amazon-S3-Bucket gespeichert ist.

import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import software.amazon.awssdk.services.rekognition.model.NotificationChannel;
import software.amazon.awssdk.services.rekognition.model.StartPersonTrackingRequest;
import software.amazon.awssdk.services.rekognition.model.Video;
import software.amazon.awssdk.services.rekognition.model.StartPersonTrackingResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.GetPersonTrackingResponse;
import software.amazon.awssdk.services.rekognition.model.GetPersonTrackingRequest;
import software.amazon.awssdk.services.rekognition.model.VideoMetadata;
import software.amazon.awssdk.services.rekognition.model.PersonDetection;
import java.util.List;

/**
 * Before running this Java V2 code example, set up your development
 * environment, including your credentials.
 *
 * For more information, see the following documentation topic:
 *
 * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
 */
public class VideoPersonDetection {
    private static String startJobId = "";

    public static void main(String[] args) {

        final String usage = """

                Usage:    <bucket> <video> <topicArn> <roleArn>

                Where:
                   bucket - The name of the bucket in which the video is located (for example, (for example, myBucket).\s
                   video - The name of video (for example, people.mp4).\s
                   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic.\s
                   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use.\s
                """;

        if (args.length != 4) {
            System.out.println(usage);
            System.exit(1);
        }

        String bucket = args[0];
        String video = args[1];
        String topicArn = args[2];
        String roleArn = args[3];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        NotificationChannel channel = NotificationChannel.builder()
                .snsTopicArn(topicArn)
                .roleArn(roleArn)
                .build();

        startPersonLabels(rekClient, channel, bucket, video);
        getPersonDetectionResults(rekClient);
        System.out.println("This example is done!");
        rekClient.close();
    }

    public static void startPersonLabels(RekognitionClient rekClient,
            NotificationChannel channel,
            String bucket,
            String video) {
        try {
            S3Object s3Obj = S3Object.builder()
                    .bucket(bucket)
                    .name(video)
                    .build();

            Video vidOb = Video.builder()
                    .s3Object(s3Obj)
                    .build();

            StartPersonTrackingRequest personTrackingRequest = StartPersonTrackingRequest.builder()
                    .jobTag("DetectingLabels")
                    .video(vidOb)
                    .notificationChannel(channel)
                    .build();

            StartPersonTrackingResponse labelDetectionResponse = rekClient.startPersonTracking(personTrackingRequest);
            startJobId = labelDetectionResponse.jobId();

        } catch (RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }

    public static void getPersonDetectionResults(RekognitionClient rekClient) {
        try {
            String paginationToken = null;
            GetPersonTrackingResponse personTrackingResult = null;
            boolean finished = false;
            String status;
            int yy = 0;

            do {
                if (personTrackingResult != null)
                    paginationToken = personTrackingResult.nextToken();

                GetPersonTrackingRequest recognitionRequest = GetPersonTrackingRequest.builder()
                        .jobId(startJobId)
                        .nextToken(paginationToken)
                        .maxResults(10)
                        .build();

                // Wait until the job succeeds
                while (!finished) {

                    personTrackingResult = rekClient.getPersonTracking(recognitionRequest);
                    status = personTrackingResult.jobStatusAsString();

                    if (status.compareTo("SUCCEEDED") == 0)
                        finished = true;
                    else {
                        System.out.println(yy + " status is: " + status);
                        Thread.sleep(1000);
                    }
                    yy++;
                }

                finished = false;

                // Proceed when the job is done - otherwise VideoMetadata is null.
                VideoMetadata videoMetaData = personTrackingResult.videoMetadata();

                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());
                System.out.println("Job");

                List<PersonDetection> detectedPersons = personTrackingResult.persons();
                for (PersonDetection detectedPerson : detectedPersons) {
                    long seconds = detectedPerson.timestamp() / 1000;
                    System.out.print("Sec: " + seconds + " ");
                    System.out.println("Person Identifier: " + detectedPerson.person().index());
                    System.out.println();
                }

            } while (personTrackingResult != null && personTrackingResult.nextToken() != null);

        } catch (RekognitionException | InterruptedException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

Erkennen von Gesichtern in einem Video, das in einem Amazon-S3-Bucket gespeichert ist.

suspend fun startFaceDetection(
    channelVal: NotificationChannel?,
    bucketVal: String,
    videoVal: String,
) {
    val s3Obj =
        S3Object {
            bucket = bucketVal
            name = videoVal
        }
    val vidOb =
        Video {
            s3Object = s3Obj
        }

    val request =
        StartFaceDetectionRequest {
            jobTag = "Faces"
            faceAttributes = FaceAttributes.All
            notificationChannel = channelVal
            video = vidOb
        }

    RekognitionClient { region = "us-east-1" }.use { rekClient ->
        val startLabelDetectionResult = rekClient.startFaceDetection(request)
        startJobId = startLabelDetectionResult.jobId.toString()
    }
}

suspend fun getFaceResults() {
    var finished = false
    var status: String
    var yy = 0
    RekognitionClient { region = "us-east-1" }.use { rekClient ->
        var response: GetFaceDetectionResponse? = null

        val recognitionRequest =
            GetFaceDetectionRequest {
                jobId = startJobId
                maxResults = 10
            }

        // Wait until the job succeeds.
        while (!finished) {
            response = rekClient.getFaceDetection(recognitionRequest)
            status = response.jobStatus.toString()
            if (status.compareTo("SUCCEEDED") == 0) {
                finished = true
            } else {
                println("$yy status is: $status")
                delay(1000)
            }
            yy++
        }

        // Proceed when the job is done - otherwise VideoMetadata is null.
        val videoMetaData = response?.videoMetadata
        println("Format: ${videoMetaData?.format}")
        println("Codec: ${videoMetaData?.codec}")
        println("Duration: ${videoMetaData?.durationMillis}")
        println("FrameRate: ${videoMetaData?.frameRate}")

        // Show face information.
        response?.faces?.forEach { face ->
            println("Age: ${face.face?.ageRange}")
            println("Face: ${face.face?.beard}")
            println("Eye glasses: ${face?.face?.eyeglasses}")
            println("Mustache: ${face.face?.mustache}")
            println("Smile: ${face.face?.smile}")
        }
    }
}

Erkennen von unangemessenen oder anstößigen Inhalten in einem Video, das in einem Amazon-S3-Bucket gespeichert ist.

suspend fun startModerationDetection(
    channel: NotificationChannel?,
    bucketVal: String?,
    videoVal: String?,
) {
    val s3Obj =
        S3Object {
            bucket = bucketVal
            name = videoVal
        }
    val vidOb =
        Video {
            s3Object = s3Obj
        }
    val request =
        StartContentModerationRequest {
            jobTag = "Moderation"
            notificationChannel = channel
            video = vidOb
        }

    RekognitionClient { region = "us-east-1" }.use { rekClient ->
        val startModDetectionResult = rekClient.startContentModeration(request)
        startJobId = startModDetectionResult.jobId.toString()
    }
}

suspend fun getModResults() {
    var finished = false
    var status: String
    var yy = 0
    RekognitionClient { region = "us-east-1" }.use { rekClient ->
        var modDetectionResponse: GetContentModerationResponse? = null

        val modRequest =
            GetContentModerationRequest {
                jobId = startJobId
                maxResults = 10
            }

        // Wait until the job succeeds.
        while (!finished) {
            modDetectionResponse = rekClient.getContentModeration(modRequest)
            status = modDetectionResponse.jobStatus.toString()
            if (status.compareTo("SUCCEEDED") == 0) {
                finished = true
            } else {
                println("$yy status is: $status")
                delay(1000)
            }
            yy++
        }

        // Proceed when the job is done - otherwise VideoMetadata is null.
        val videoMetaData = modDetectionResponse?.videoMetadata
        println("Format: ${videoMetaData?.format}")
        println("Codec: ${videoMetaData?.codec}")
        println("Duration: ${videoMetaData?.durationMillis}")
        println("FrameRate: ${videoMetaData?.frameRate}")

        modDetectionResponse?.moderationLabels?.forEach { mod ->
            val seconds: Long = mod.timestamp / 1000
            print("Mod label: $seconds ")
            println(mod.moderationLabel)
        }
    }
}